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Usefulness of the Information Contained in the Prediction Sample for the Spatial Error Model

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  • Takafumi Kato

Abstract

A recent study proposed an estimation approach that uses data on the independent variables and location for the prediction sample, and suggested that it may improve estimation and prediction. This is an incomplete data approach following an iterative process along the lines of the EM algorithm. The present study compares this approach with a partial data approach that uses only data on the dependent and independent variables and location for the estimation sample. Our Monte Carlo experiments show that unless the estimation and prediction samples constitute the whole population and the data generating model is used as the data fitting model, the incomplete data approach is not guaranteed to be superior to the partial data approach. Copyright Springer Science+Business Media, LLC 2013

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  • Takafumi Kato, 2013. "Usefulness of the Information Contained in the Prediction Sample for the Spatial Error Model," The Journal of Real Estate Finance and Economics, Springer, vol. 47(1), pages 169-195, July.
    • Handle: RePEc:kap:jrefec:v:47:y:2013:i:1:p:169-195
    DOI: 10.1007/s11146-011-9345-9
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    1. James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, September.
    2. Takafumi Kato, 2008. "Response To Comment On “A Further Exploration Into The Robustness Of Spatial Autocorrelation Specifications”," Journal of Regional Science, Wiley Blackwell, vol. 48(3), pages 651-653, August.
    3. Brasington, David M. & Hite, Diane, 2005. "Demand for environmental quality: a spatial hedonic analysis," Regional Science and Urban Economics, Elsevier, vol. 35(1), pages 57-82, January.
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    6. Takafumi Kato, 2008. "A Further Exploration Into The Robustness Of Spatial Autocorrelation Specifications," Journal of Regional Science, Wiley Blackwell, vol. 48(3), pages 615-639, August.
    7. Pace, R Kelley & Gilley, Otis W, 1997. "Using the Spatial Configuration of the Data to Improve Estimation," The Journal of Real Estate Finance and Economics, Springer, vol. 14(3), pages 333-340, May.
    8. Gawande, Kishore & Jenkins-Smith, Hank, 2001. "Nuclear Waste Transport and Residential Property Values: Estimating the Effects of Perceived Risks," Journal of Environmental Economics and Management, Elsevier, vol. 42(2), pages 207-233, September.
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    11. Bradford Case & John Clapp & Robin Dubin & Mauricio Rodriguez, 2004. "Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 167-191, September.
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    Cited by:

    1. Suesse, Thomas, 2018. "Marginal maximum likelihood estimation of SAR models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 98-110.
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    3. Takafumi Kato, 2020. "Likelihood-based strategies for estimating unknown parameters and predicting missing data in the simultaneous autoregressive model," Journal of Geographical Systems, Springer, vol. 22(1), pages 143-176, January.

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    More about this item

    Keywords

    Estimation sample; Prediction sample; Population; Data generating model; Data fitting model; C13; C21; C53;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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